Large language models (LLMs) have greatly accelerated the automation of algorithm generation and optimization. However, current methods such as EoH and FunSearch mainly rely on predefined templates and expert-specified functions that focus solely on the local evolution of key functionalities. Consequently, they fail to fully leverage the synergistic benefits of the overall architecture and the potential of global optimization. In this paper, we introduce an end-to-end algorithm generation and optimization framework based on LLMs. Our approach utilizes the deep semantic understanding of LLMs to convert natural language requirements or human-authored papers into code solutions, and employs a two-dimensional co-evolution strategy to optimize both functional and structural aspects. This closed-loop process spans problem analysis, code generation, and global optimization, automatically identifying key algorithm modules for multi-level joint optimization and continually enhancing performance and design innovation. Extensive experiments demonstrate that our method outperforms traditional local optimization approaches in both performance and innovation, while also exhibiting strong adaptability to unknown environments and breakthrough potential in structural design. By building on human research, our framework generates and optimizes novel algorithms that surpass those designed by human experts, broadening the applicability of LLMs for algorithm design and providing a novel solution pathway for automated algorithm development.

翻译：大型语言模型（LLMs）极大地加速了算法生成与优化的自动化进程。然而，当前如EoH和FunSearch等方法主要依赖于预定义模板和专家指定的函数，仅关注关键功能的局部演化。因此，这些方法未能充分利用整体架构的协同优势以及全局优化的潜力。本文提出一种基于LLMs的端到端算法生成与优化框架。该方法利用LLMs的深度语义理解能力，将自然语言需求或人工撰写的论文转化为代码解决方案，并采用二维协同演化策略同时优化功能与结构两方面。这一闭环流程涵盖问题分析、代码生成与全局优化，能够自动识别关键算法模块以进行多级联合优化，并持续提升性能与设计创新性。大量实验表明，本方法在性能与创新性上均优于传统的局部优化方法，同时对未知环境表现出强适应性，并在结构设计上具备突破潜力。通过基于人类研究成果进行构建，本框架能够生成并优化出超越人类专家设计的新型算法，从而拓宽了LLMs在算法设计领域的适用性，为自动化算法开发提供了一条新颖的解决路径。